Machine learning prediction of postoperative major adverse cardiovascular events in geriatric patients: a prospective cohort study.

Journal: BMC anesthesiology
Published Date: Sep 10, 2022

Abstract

BACKGROUND: Postoperative major adverse cardiovascular events (MACEs) account for more than one-third of perioperative deaths. Geriatric patients are more vulnerable to postoperative MACEs than younger patients. Identifying high-risk patients in advance can help with clinical decision making and improve prognosis. This study aimed to develop a machine learning model for the preoperative prediction of postoperative MACEs in geriatric patients.

Authors

  • Xiran Peng
    Department of Anesthesiology, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, PO Box 610041, Chengdu, China.
  • Tao Zhu
    Wuhan Zoncare Bio-Medical Electronics Co., Ltd, Wuhan, China.
  • Tong Wang
    School of Public Health, Shanxi Medical University, Taiyuan 030000, China; Key Laboratory of Coal Environmental Pathogenicity and Prevention (Shanxi Medical University), Ministry of Education, Taiyuan 030000, China.
  • Fengjun Wang
    Center of Statistical Research, School of Statistics, Southwestern University of Finance and Economics, Chengdu, China.
  • Ke Li
    School of Ideological and Political Education, Shanghai Maritime University, Shanghai, China.
  • Xuechao Hao
    Department of Anesthesiology, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, PO Box 610041, Chengdu, China. aneshxc@163.com.

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